Seec Corporation

TCAT SHELBYVILLE INSTITUTIONAL CARES ACT APPLICATION

CARES ACT FOR TCAT SHELBYVILLE

SBIR PHASE II: REAL-TIME COMMUNITY-IN-THE-LOOP PLATFORM FOR IMPROVED URBAN FLOOD FORECASTING AND MANAGEMENT -THE BROADER/COMMERCIAL IMPACT OF THIS SMALL BUSINESS INNOVATION RESEARCH (SBIR) PHASE II PROJECT IS IN THE POTENTIAL TO TRANSFORM FLOOD MANAGEMENT TOOLS BY COMBINING COMMUNITY INSIGHTS WITH ARTIFICIAL INTELLIGENCE TO GENERATE INFORMATION ON URBAN FLOOD DYNAMICS FROM THOSE EXPERIENCING FLOOD IMPACTS. CHANGING HYDROLOGICAL CYCLES, SEA LEVEL RISE AND INADEQUATE INFRASTRUCTURE HAVE MADE URBAN FLOODING A GLOBAL ISSUE. THIS PROJECT WILL IMPROVE EFFICIENCY AND SPEED OF FLOOD RESPONSES AND DESIGN OF URBAN FLOOD MANAGEMENT INFRASTRUCTURE BY COMBINING DISPARATE DATA SOURCES INCLUDING RESIDENT POSTS ON LOCAL FLOOD AND GEOSPATIAL DATA ON INFRASTRUCTURE AND COMMUNITY CHARACTERISTICS. IT AIMS TO IMPROVE ENVIRONMENTAL JUSTICE BY GIVING RESIDENTS THE ABILITY TO REPORT FLOOD INCIDENTS AND IMPACTS IN DATA THAT CAN BE USED BY STORMWATER MANAGERS. BY TRACKING REAL-TIME IMPACTS IN AREAS MOST VULNERABLE TO FLOODING, WHICH DISPROPORTIONATELY AFFECT MARGINALIZED COMMUNITIES, IT WOULD HELP CITIES RESPOND MORE EFFICIENTLY TO FLOODING EVENTS, PRIORITIZE FLOOD ADAPTATION MAINTENANCE, AND FACILITATE STEWARDSHIP TO IMPROVE THE HEALTH AND WELL-BEING OF UNDERSERVED COMMUNITIES. THIS PROJECT SERVES AS A TECHNICAL PLATFORM FOR NOVEL MULTI-SECTOR APPROACHES CRITICAL FOR THE EFFECTIVE IMPLEMENTATION OF CLIMATE SOLUTIONS. BY ENGAGING DIRECTLY WITH THE PUBLIC, THE PROJECT EDUCATES USERS ON LOCAL CLIMATE RISKS AND MITIGATION STRATEGIES. THE GOAL OF THIS PROJECT IS TO IMPROVE FLOOD INCIDENT RESPONSE AND INFRASTRUCTURE PLANNING BY CITIES, COUNTIES, AND UTILITIES BY PROVIDING HYPER-LOCAL COMMUNITY-GENERATED DATA AND ARTIFICIAL INTELLIGENCE (AI) ENABLED FLOOD IMPACT INSIGHTS NOT ACCESSIBLE WITH CURRENT APPROACHES. THE SYNTHESIS OF MULTIPLE FORMS OF ENVIRONMENTAL AND COMMUNITY-GENERATED DATA INTO QUANTITATIVE INSIGHTS FOR STORMWATER MANAGERS REPRESENTS A SIGNIFICANT TECHNICAL CHALLENGE. THIS PROJECT AIMS TO FILL CRITICAL DATA GAPS BY DEVELOPING ACCURATE ALGORITHMS FOR EXTRACTING FLOOD HEIGHT, DETAILED FLOOD CHARACTERISTICS, PERSONAL IMPACTS, AND ROOT CAUSES FOR FLOODING OF ALL SEVERITY LEVELS, AS WELL AS METHODS TO AGGREGATE INFORMATION FROM DIFFERENT SOURCES AND MODALITIES. COMBINED WITH AN AUTOMATED PROMPTING WORKFLOW, THE TOOL WILL PROVIDE A PLATFORM FOR POSITIVE REINFORCEMENT FEEDBACK FOR IMPROVING THE DATA QUALITY, COVERAGE, AND ENGAGEMENT ACROSS RESIDENTS AND FLOOD MANAGERS IN FLOOD PRONE AREAS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.

SBIR PHASE I: DEEP LEARNING FRAMEWORK FOR MAPPING FLOOD EXTENT FROM UNSTRUCTURED PHOTOS -THE BROADER/ COMMERCIAL IMPACT OF THIS SMALL BUSINESS INNOVATION RESEARCH (SBIR) PHASE I PROJECT IS IN HELPING COMMUNITIES HANDLE FLOODS BETTER BY TURNING PUBLIC PHOTOS AND VIDEOS INTO NEAR-REAL-TIME USEFUL DATA. CURRENTLY, IT IS DIFFICULT TO GET A CLEAR PICTURE DURING EMERGENCIES BECAUSE SENSORS AND SATELLITES ARE OFTEN TOO FAR AWAY OR FIELD SURVEYS ARE TOO EXPENSIVE. THIS SYSTEM USES SMARTPHONE IMAGES TO CREATE REAL-TIME MAPS THAT HELP RESCUERS AND CITY PLANNERS SEE EXACTLY WHERE THE WATER IS. THIS TECHNOLOGY MAKES FLOOD RESPONSE FASTER AND MORE ACCURATE, WHICH HELPS SAVE LIVES AND REDUCE PROPERTY DAMAGE. FOR BUSINESSES, THIS TOOL PROVIDES A SCALABLE SERVICE FOR EMERGENCY RESPONDERS, INFRASTRUCTURE PLANNERS, UTILITIES, AND INSURANCE COMPANIES, CREATING NEW JOBS IN THE TECH INDUSTRY. OVERALL, THIS PROJECT CREATES A MORE AFFORDABLE AND EFFECTIVE WAY FOR THE NATION TO MANAGE FLOOD RISKS USING THE PHOTOS PEOPLE ALREADY TAKE ON THEIR PHONES. THE CORE TECHNICAL INNOVATION OF THIS PROJECT IS A HIGH-RISK, DIFFICULT-TO-REPLICATE METHOD FOR AUTOMATICALLY CONVERTING INDIVIDUAL FLOOD PHOTOGRAPHS INTO PRECISE, GEOREFERENCED FLOOD EXTENT MAPS USING ARTIFICIAL INTELLIGENCE. THIS EFFORT UTILIZES ADVANCED COMPUTER VISION TO IDENTIFY AND MATCH GROUND CONTROL POINTS BETWEEN UNSTRUCTURED IMAGES AND GEOGRAPHIC MAPS, A PROCESS THAT HAS HISTORICALLY REQUIRED TIME-CONSUMING MANUAL INTERVENTION. THIS INNOVATION IS PARTICULARLY CHALLENGING BECAUSE IT REQUIRES IDENTIFYING STABLE REFERENCE FEATURES IN UNSTRUCTURED IMAGES THAT MAY BE NOISY OR PARTIALLY OBSCURED BY FLOODWATER. THE PHASE I RESEARCH FOCUSES ON OVERCOMING THE PRIMARY TECHNICAL BARRIER: AUTOMATICALLY IDENTIFYING REFERENCE FEATURES IN UNSTRUCTURED IMAGES AND MATCHING THEM TO EXISTING GEOSPATIAL DATA. THE RESEARCH SCOPE INCLUDES AUTOMATING IMAGE SEGMENTATION FOR FLOODED AREAS, DEVELOPING AND TESTING DEEP LEARNING MODELS THAT DETECT VISUAL FEATURES AND ALIGN THEM WITH MAPPED REFERENCE POINTS, AND LEVERAGING MONOPLOTTING TECHNIQUES TO PROJECT OBSERVED FLOOD BOUNDARIES ONTO ELEVATION MAPS. THE RESULTING LOCALIZED FLOOD EXTENTS WILL THEN BE COMBINED TO FORM CONTINUOUS MAPS OF PEAK INUNDATION AT THE STORM LEVEL. THE WORK WILL DEMONSTRATE TECHNICAL FEASIBILITY, QUANTIFY ACCURACY, AND REDUCE UNCERTAINTY IN AN AUTOMATED WORKFLOW SUITABLE FOR OPERATIONAL USE. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.

SBIR PHASE I: USER-GENERATED REAL TIME QUALITATIVE DATA PROCESSING FOR CLIMATE IMPACTED MODEL VALIDATION, INTEGRATION, AND AUGMENTATION -THE BROADER IMPACT OF THIS SBIR PHASE I PROJECT IS THE DEVELOPMENT OF AN INTEGRATED METHODOLOGY TO USE RESIDENT?S EXPERIENCES ABOUT FLOOD (AND OTHER CLIMATE CHANGE) EVENTS TO VALIDATE MODELING IN REAL TIME, INFORM POLICY, AND PROVIDE DESIGN INSIGHTS FOR INFRASTRUCTURE DEVELOPMENT. IT PROVIDES AN INTEGRATED SOLUTION FOR CAPTURING THE VALUABLE INFORMATION CAPTURED BY PEOPLE?S DIRECT EXPERIENCES (PHOTOS, STORIES, AND DATA) WITH CLIMATE CHANGE THAT ARE OTHERWISE UNDERUTILIZED. THE TEAM WILL DEVELOP A COMMUNITY KNOWLEDGE PLATFORM THAT CAN PROCESS A MIX OF TEXT AND PHOTO DATA SUBMITTED BY RESIDENTS AND PROCESS IT INTO FORMATS USABLE FOR UNDERSTANDING ON-THE-GROUND IMPACTS, FLOOD OCCURRENCE AND SEVERITY, AND DELIVER THAT DATA TO PLANNERS AND MODELERS DEVELOPING WAYS TO BETTER MANAGE FLOODS. DATA PROCESSING OCCURS BEHIND THE SCENES AND ALLOWS RESIDENTS TO ENGAGE WITH THE PLANNING PROCESSES IMPACTING THEIR COMMUNITIES IN NEW WAYS, INCREASE THE ACCESS OF UNDERREPRESENTED COMMUNITIES, AND IMPROVE EQUITY IN DECISION-MAKING. THE PROJECT WILL STIMULATE RESEARCH IN DATA SCIENCES, GENERATE NEW TYPES OF JOBS IN CIVIC DATA SYSTEMS, AND IMPROVE THE EFFICIENCY OF PUBLIC INFRASTRUCTURE INVESTMENTS. USER?S CELL PHONES WILL BECOME POWERFUL LOCAL DATA COLLECTION TOOLS ALLOWING A DIRECT LINE OF COMMUNICATIONS AND BUILDING TRUST BETWEEN GOVERNMENT DECISION MAKERS, SCIENTISTS, AND RESIDENTS. ADVANCES IN DATA SCIENCE ALLOWS THE ANALYSIS OF HETEROGENEOUS QUALITATIVE AND IMAGE DATA TO INCORPORATE USER GENERATED POSTS INTO LARGE SCALE INFRASTRUCTURE PLANNING AROUND CLIMATE RESILIENCE. CURRENTLY, DESCRIPTIVE DATA AND PHOTOS SUBMITTED BY USERS ARE MANUALLY ANALYZED FOR CONTENT. THROUGH NOVEL USE OF NATURAL LANGUAGE PROCESSING (NLP), SPATIAL DATA ANALYSIS, ARTIFICIAL INTELLIGENT (AI) AND COMPUTER VISION OF FLOOD EVENT PHOTOS, AND DEVELOPMENT OF AN APPLICATION PROGRAMMING INTERFACE (API) TO CURATE DATA FOR HYDROLOGICAL MODEL DEVELOPERS, THIS PROJECT AUTOMATES THE PROCESS OF EXTRACTING THE FULL VALUE OF COMMUNITY GENERATED POSTS OF FLOOD EVENTS. WHEN SUCCESSFUL, HYPERLOCAL USER GENERATED POSTS WILL BE PROCESSED IN REAL TIME TO DELIVER DETAILED ON-THE-GROUND DATA ON FLOOD EVENTS TO PLANNERS, FOR MODEL VALIDATION, AND COMMUNITY MEMBERS THEMSELVES. THE PRODUCT BUILDS INNOVATIVE TECHNOLOGIES TO PERMIT PROCESSING AT SCALE SO THAT ANY COMMUNITY EXPERIENCING FLOOD EVENTS CAN GENERATE REAL TIME FLOOD DATA AND MONITOR THE IMPACT OF INFRASTRUCTURE AS HYDROLOGICAL BASELINES CONTINUE TO SHIFT. THE PROJECT DEVELOPS NEW MACHINE LEARNING NLP TO AUTOMATE THE ANALYSIS OF QUALITATIVE TEXT DATA, KEYWORD DETECTION FOR SENTIMENT ANALYSIS AND IMPACT, AI TO EXTRACT FLOOD CHARACTERISTICS FROM PHOTOS, AND API FOR PROTECTING MODEL IP WHILE ALLOWING INTEGRATION WITH EXTERNAL DATA FOR VALIDATION PURPOSES. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.

RBDG RURAL BUSINESS COOP RURAL ENTERPRISE GRANT

ECA PROVIDED FUNDING TO SUPPORT AND EXPAND THE FULBRIGHT CENTER'S ALUMNI OUTREACH ACTIVITIES BY PARTIALLY PAYING THE SALARY OF ONE OF THE TWO ALUMNI

RBDG RURAL BUSINESS COOP RURAL ENTERPRISE GRANT

COVER FULBRIGHT GRANTEES EXPENSES FOR THE 5TH ANNUAL FULBRIGHT FORUM.

AMERICAN VOICES SEMINAR IN WHICH U.S. FULBRIGHT GRANTEES IN FINLAND PRESENT TOPICS ON AMERICAN SOCIETY AND CULTURE.

AMERICAN VOICES SEMINAR 2018

TO PARTIALLY COVER SEMINAR ORGANIZING COSTS, INCLUDING SALARY AND MATERIAL FEES, AND TRAVEL/LODGING COSTS FOR SPEAKERS